I kept running into the same problem: some of the best AI/ML books are legally free. The authors put them up on their own sites, but the links are scattered across personal pages, university sites, and random GitHub repos nobody finds.
So I built a single index: Awesome Free AI Books. 30+ books across Deep Learning, Reinforcement Learning, Bayesian/Probabilistic ML, NLP & LLMs, Math for ML, Computer Vision, Generative Models, Causal Inference, GNNs, and AI Safety. Think Goodfellow’s Deep Learning, Sutton & Barto’s RL bible, Murphy’s Probabilistic ML, Bishop’s latest, Jurafsky & Martin’s SLP3 draft, and more.
Every link points straight to the author’s or publisher’s own page—no rehosted PDFs, no shady mirrors. A weekly GitHub Action checks all links so they don't rot over time. 🔄
It’s open source and open to contributions. If you know a legitimately free book that’s missing, PRs and issues are welcome. 🤝
Repo:
https://github.com/MarcosSete/awesome-free-ai-books
#AI #MachineLearning #DeepLearning #NLP #LLMs #OpenSource
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So I built a single index: Awesome Free AI Books. 30+ books across Deep Learning, Reinforcement Learning, Bayesian/Probabilistic ML, NLP & LLMs, Math for ML, Computer Vision, Generative Models, Causal Inference, GNNs, and AI Safety. Think Goodfellow’s Deep Learning, Sutton & Barto’s RL bible, Murphy’s Probabilistic ML, Bishop’s latest, Jurafsky & Martin’s SLP3 draft, and more.
Every link points straight to the author’s or publisher’s own page—no rehosted PDFs, no shady mirrors. A weekly GitHub Action checks all links so they don't rot over time. 🔄
It’s open source and open to contributions. If you know a legitimately free book that’s missing, PRs and issues are welcome. 🤝
Repo:
https://github.com/MarcosSete/awesome-free-ai-books
#AI #MachineLearning #DeepLearning #NLP #LLMs #OpenSource
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❤4
Day 7 of self-studying Berkeley CS189 — stochastic gradient descent notes 📚📝
🔥 *Stochastic Gradient Descent (SGD)* is a powerful optimization algorithm used to minimize loss functions in machine learning. Unlike batch gradient descent, which uses the entire dataset to compute gradients, SGD updates parameters using a single training example (or a small mini-batch) at a time.
🚀 Key Benefits:
- Faster convergence on large datasets
- Escapes local minima more easily
- Suitable for online learning scenarios
📊 The Update Rule:
Where
📌 Challenges:
- High variance in updates
- Requires careful tuning of the learning rate
🧠 *Tip:* Use momentum or adaptive learning rates (like Adam) to stabilize training!
#MachineLearning #CS189 #SGD #DeepLearning #DataScience #Algorithms
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🔥 *Stochastic Gradient Descent (SGD)* is a powerful optimization algorithm used to minimize loss functions in machine learning. Unlike batch gradient descent, which uses the entire dataset to compute gradients, SGD updates parameters using a single training example (or a small mini-batch) at a time.
🚀 Key Benefits:
- Faster convergence on large datasets
- Escapes local minima more easily
- Suitable for online learning scenarios
📊 The Update Rule:
θ = θ - α * ∇J(θ; x⁽ⁱ⁾, y⁽ⁱ⁾)Where
α is the learning rate and (x⁽ⁱ⁾, y⁽ⁱ⁾) is a single training example.📌 Challenges:
- High variance in updates
- Requires careful tuning of the learning rate
🧠 *Tip:* Use momentum or adaptive learning rates (like Adam) to stabilize training!
#MachineLearning #CS189 #SGD #DeepLearning #DataScience #Algorithms
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❤5
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🔖 Learning Data Science through interactive examples
One of the most useful repositories for those who want to better understand machine learning.
It transforms complex concepts into visual experiments: you can study models, change parameters, and immediately see the results.
⛓ Link to GitHub
https://github.com/GeostatsGuy/DataScienceInteractivePython
#DataScience #MachineLearning #Python #Learning #Tech #GitHub
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⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
One of the most useful repositories for those who want to better understand machine learning.
It transforms complex concepts into visual experiments: you can study models, change parameters, and immediately see the results.
⛓ Link to GitHub
https://github.com/GeostatsGuy/DataScienceInteractivePython
#DataScience #MachineLearning #Python #Learning #Tech #GitHub
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🔖 Over 300 real-world case studies of ML systems from top companies. 🤖
We found a repository that collects genuine ML engineering experience – not theory from textbooks, but real stories of implementing models in production. 📚
Inside, you'll find case studies from Uber, Netflix, Google, and other companies: how they built the architecture, what problems arose, where the systems failed, and what solutions helped them recover. 🏗️
⛓ Link to GitHub
https://github.com/Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies
#MachineLearning #MLCaseStudies #DataScience #Engineering #Uber #Netflix
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
We found a repository that collects genuine ML engineering experience – not theory from textbooks, but real stories of implementing models in production. 📚
Inside, you'll find case studies from Uber, Netflix, Google, and other companies: how they built the architecture, what problems arose, where the systems failed, and what solutions helped them recover. 🏗️
⛓ Link to GitHub
https://github.com/Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies
#MachineLearning #MLCaseStudies #DataScience #Engineering #Uber #Netflix
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
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❤2
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